Architectures of Meaning, A Systematic Corpus Analysis of NLP Systems
- URL: http://arxiv.org/abs/2107.08124v1
- Date: Fri, 16 Jul 2021 21:10:43 GMT
- Title: Architectures of Meaning, A Systematic Corpus Analysis of NLP Systems
- Authors: Oskar Wysocki, Malina Florea, Donal Landers and Andre Freitas
- Abstract summary: The framework is validated in the full corpus of Semeval tasks.
It provides a systematic mechanism to interpret a largely dynamic and exponentially growing field.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper proposes a novel statistical corpus analysis framework targeted
towards the interpretation of Natural Language Processing (NLP) architectural
patterns at scale. The proposed approach combines saturation-based lexicon
construction, statistical corpus analysis methods and graph collocations to
induce a synthesis representation of NLP architectural patterns from corpora.
The framework is validated in the full corpus of Semeval tasks and demonstrated
coherent architectural patterns which can be used to answer architectural
questions on a data-driven fashion, providing a systematic mechanism to
interpret a largely dynamic and exponentially growing field.
Related papers
- Mathematical Derivation Graphs: A Task for Summarizing Equation Dependencies in STEM Manuscripts [1.1961645395911131]
We take the initial steps toward understanding the dependency relationships between mathematical expressions in STEM articles.
Our dataset, sourced from a random sampling of the arXiv corpus, contains an analysis of 107 published STEM manuscripts.
We exhaustively evaluate analytical and NLP-based models to assess their capability to identify and extract the derivation relationships for each article.
arXiv Detail & Related papers (2024-10-26T16:52:22Z) - Customized Information and Domain-centric Knowledge Graph Construction with Large Language Models [0.0]
We propose a novel approach based on knowledge graphs to provide timely access to structured information.
Our framework encompasses a text mining process, which includes information retrieval, keyphrase extraction, semantic network creation, and topic map visualization.
We apply our methodology to the domain of automotive electrical systems to demonstrate the approach, which is scalable.
arXiv Detail & Related papers (2024-09-30T07:08:28Z) - Interactive Topic Models with Optimal Transport [75.26555710661908]
We present EdTM, as an approach for label name supervised topic modeling.
EdTM models topic modeling as an assignment problem while leveraging LM/LLM based document-topic affinities.
arXiv Detail & Related papers (2024-06-28T13:57:27Z) - Unsupervised Graph Neural Architecture Search with Disentangled
Self-supervision [51.88848982611515]
Unsupervised graph neural architecture search remains unexplored in the literature.
We propose a novel Disentangled Self-supervised Graph Neural Architecture Search model.
Our model is able to achieve state-of-the-art performance against several baseline methods in an unsupervised manner.
arXiv Detail & Related papers (2024-03-08T05:23:55Z) - An Encoding of Abstract Dialectical Frameworks into Higher-Order Logic [57.24311218570012]
This approach allows for the computer-assisted analysis of abstract dialectical frameworks.
Exemplary applications include the formal analysis and verification of meta-theoretical properties.
arXiv Detail & Related papers (2023-12-08T09:32:26Z) - Constructing Word-Context-Coupled Space Aligned with Associative
Knowledge Relations for Interpretable Language Modeling [0.0]
The black-box structure of the deep neural network in pre-trained language models seriously limits the interpretability of the language modeling process.
A Word-Context-Coupled Space (W2CSpace) is proposed by introducing the alignment processing between uninterpretable neural representation and interpretable statistical logic.
Our language model can achieve better performance and highly credible interpretable ability compared to related state-of-the-art methods.
arXiv Detail & Related papers (2023-05-19T09:26:02Z) - Nested Named Entity Recognition as Holistic Structure Parsing [92.8397338250383]
This work models the full nested NEs in a sentence as a holistic structure, then we propose a holistic structure parsing algorithm to disclose the entire NEs once for all.
Experiments show that our model yields promising results on widely-used benchmarks which approach or even achieve state-of-the-art.
arXiv Detail & Related papers (2022-04-17T12:48:20Z) - Design considerations for a hierarchical semantic compositional
framework for medical natural language understanding [3.7003326903946756]
We describe a framework inspired by mechanisms of human cognition in an attempt to jump the NLP performance curve.
The paper describes insights from four key aspects including semantic memory, semantic composition, semantic activation.
We discuss the design of a generative semantic model and an associated semantic used to transform a free-text sentence into a logical representation of its meaning.
arXiv Detail & Related papers (2022-04-05T09:04:34Z) - Structural Landmarking and Interaction Modelling: on Resolution Dilemmas
in Graph Classification [50.83222170524406]
We study the intrinsic difficulty in graph classification under the unified concept of resolution dilemmas''
We propose SLIM'', an inductive neural network model for Structural Landmarking and Interaction Modelling.
arXiv Detail & Related papers (2020-06-29T01:01:42Z) - Neural Entity Linking: A Survey of Models Based on Deep Learning [82.43751915717225]
This survey presents a comprehensive description of recent neural entity linking (EL) systems developed since 2015.
Its goal is to systemize design features of neural entity linking systems and compare their performance to the remarkable classic methods on common benchmarks.
The survey touches on applications of entity linking, focusing on the recently emerged use-case of enhancing deep pre-trained masked language models.
arXiv Detail & Related papers (2020-05-31T18:02:26Z) - Exploring Probabilistic Soft Logic as a framework for integrating
top-down and bottom-up processing of language in a task context [0.6091702876917279]
The architecture integrates existing NLP components to produce candidate analyses on eight levels of linguistic modeling.
The architecture builds on Universal Dependencies (UD) as its representation formalism on the form level and on Abstract Meaning Representations (AMRs) to represent semantic analyses of learner answers.
arXiv Detail & Related papers (2020-04-15T11:00:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.